The public health impact of COVID-19 variants of concern on the effectiveness of contact tracing in Vermont, United States

Case investigation and contact tracing (CICT) are public health measures that aim to break the chain of pathogen transmission. Changes in viral characteristics of COVID-19 variants have likely affected the effectiveness of CICT programs. We estimated and compared the cases averted in Vermont when the original COVID-19 strain circulated (Nov. 25, 2020–Jan. 19, 2021) with two periods when the Delta strain dominated (Aug. 1–Sept. 25, 2021, and Sept. 26–Nov. 20, 2021). When the original strain circulated, we estimated that CICT prevented 7180 cases (55% reduction in disease burden), compared to 1437 (15% reduction) and 9970 cases (40% reduction) when the Delta strain circulated. Despite the Delta variant being more infectious and having a shorter latency period, CICT remained an effective tool to slow spread of COVID-19; while these viral characteristics did diminish CICT effectiveness, non-viral characteristics had a much greater impact on CICT effectiveness.

where k is approximated from the effective reproduction number (Re), since undetected infected contacts will infect Re additional individuals on average.If the assumed compliance was 100%, the estimated effectiveness could be as high as 57% for Period D1, and 54% for Period D2. # The average length of time from infection to isolation and quarantine between cases and contacts which later became cases.We assumed a 4 or 5-day pre-symptomatic period, depending on the latent period.We further assumed that interviewed cases and notified contacts to begin isolation and quarantine the day after their interactions with the health department.For more details, please refer to: Jeon et al. [2] , Technical Appendix, Figure S2.

The Effect of Vaccination
Vaccination reduces the pool of susceptible individuals that can benefit from CICT.We expect CICT's impact to diminish when the number of vaccinated individuals increases as CICT programs may be unable to identify and prioritize unvaccinated contacts.Because there were few vaccinated individuals during our earliest period of analysis (Nov. 25, 2020-Jan. 19, 2021), we expect CICT's impact to be affected by the presence of vaccinated individuals only during the two periods of analysis when the Delta strain dominated virus circulation (Aug.01 -Sept. 25, 2021, andSept. 26 -Nov. 20, 2021).To make sure we did not overestimate the impact of CICT during the two Delta periods, we removed the number of people vaccinated from the pool of susceptible individuals at the beginning of each analysis.In other words, to avoid an overestimation of CICT impact, we assumed that vaccine effectiveness was 100% in the pre-analysis period.Obviously, there are still breakthrough infections that are occurring, but this phenomenon is captured by our fitting process.Lastly, we assumed vaccine-induced immunity lasted for exactly 180 days, while in practice it fades gradually over time [15].We assessed the influence on our results of uncertainty in Vermont's vaccination levels by varying the documented level of Vermont's fully vaccinated population by ±10%.We found that the absolute number of cases averted is considerably affected by the size of the pool of susceptible individuals (Figure S1, Panel A), where susceptible individuals are defined as those who have never been infected or never vaccinated, and those whose disease-or vaccine-induced immunity has waned.While the absolute number of individuals is considerably affected by the size of the pool of susceptible individuals, the relative impact that it has on disease transmission is not affected in a meaningful way (Figure S1, Panel B).

The Effect of Public Compliance with Isolation and Quarantine
We assumed that 80% of cases who completed case interviews complied with the isolation guidelines and that 30% of contacts that are notified complied with quarantine guidelines (Table S4).We further assumed that confirmed cases who were not interviewed and contacts that were  S5.Second, we found a 20 percentage points increase in isolation for cases who did not complete case interviews (from 0% to 20%) and a 10 percentage points increase in quarantine of contacts who were not notified (from 0% to 10%), the percentage of cases averted increased from 14.5% to 15.7% for Period D1 (1.2 percentage point increase) and from 40.1% to 43.8% for Period D2 (3.7 percentage point increase); see Table S5.
The lesser impact attributable to cases and contacts who were not in touch with the Vermont Department of Health is likely because these individuals represent a very low share of total cases and contacts due to the excellent public receptivity and CICT program effectiveness in Vermont (see Table S2).

0%
Notes.Each row is a mutually exclusive group of cases or contacts.The sum of each row does not add up to 100%, as the numbers represent the assumed compliance within each group.0% compliance means none of the cases or contacts in a group isolated or quarantined effectively.100% means all cases or contacts in a group isolated or quarantined effectively after being interviewed or contacted.* Based on a review of the literature.Findings and sources were as follows: A review of multiple cross-sectional population surveys in the UK suggests that 40-45% of people who had COVID-like symptoms self-reported fully complying with isolation guidance during their infectious periods [30].A survey in the U.S. found that 85% of respondents who had COVID-like symptoms or tested positive stayed home (according to CDC guidelines) except to get medical care [31].And a third survey, also in the U.S., found that 93% of adults said they would definitely (73%) or probably (20%) quarantine themselves for at least 14 days if told to do so by a public health official because they had the coronavirus (i.e., they were confirmed cases, not just exposed contacts) [32].† Includes cases that were not reached and those that were reached but who did not agree to be interviewed.‡ Compliance was set to zero for these case/contact groups categories because any transmission reductions from quarantine and isolation are not attributable to direct interactions with the health department's CICT staff, and therefore outside of the scope of this analysis.Their inclusion here is to help distinguish between the various cases/contacts types.* Results corresponding to the base case assumed levels of public compliance levels to isolation and quarantine guidelines-see Table 2. † Because we account for the possibility that confirmed cases that did not complete case interviews might have effectively isolated, and similarly because we account for the possibility that contacts that are not notified might have effectively quarantined, we changed the wording from "CICT Impact Estimate" to "Isolation and Quarantine Impact Estimate."‡ Equivalent to the number of cases averted by CICT among every 100 cases not averted by vaccine or other nonpharmaceutical interventions (NPIs; such as facemask policies, large gathering restrictions, and school/business closures).

Technical Appendix
We use the U.S. Centers for Disease Control and Prevention (CDC)'s COVIDTracer Advanced modeling tool to estimate the public health impact of CICT in Vermont; see [2,3,4,12,33] for other papers that use or discuss this modeling tool.Using Vermont-specific data, the model simulates counterfactual epidemic curves without the CICT program (see Section on Modeling Nonpharmaceutical Interventions for more details).The difference between the observed case count and the model-generated case count without the CICT program, provides our estimate of the impact of CICT in terms of the number of cases averted (see Figure 2).We assume that transmission reductions due to nonpharmaceutical interventions (NPIs)-including CICT and other NPIs such as facemask policies, large gathering restrictions, school/business closures, etc.remain constant over the period of analysis (56 days).The disease dynamics of COVID-19 are modeled using an SIR epidemiological model (see [24] for a detailed discussion of compartments models for COVID-19, and see [34] for more details on how such models can be used to model contact tracing interventions and virus mutations), which simulates the change in the number of susceptible (S), infected (I), and removed (R) individuals over time.

Model of Disease Transmission
We use a susceptible-infected-removed (SIR) model to predict the disease dynamics of a jurisdiction (here, Vermont).We assume the jurisdiction's total population is closed in the sense that there are no exogeneous importations of infected individuals (e.g., from a neighboring state; see [35] for instance) and that the jurisdiction's population size remains constant over the period of analysis (i.e., we omit from births of susceptible individuals and deaths given the short time frame).Given the short time-horizon of our analyses (56 days), we assume immunity through vaccination (whether it is partial or full) remains at the same level over the period analyzed.This assumption simplifies the model-fitting process, as we do not have to account for complex vaccination dynamics.
In the absence of NPIs, the change in susceptible individuals between any two days is where   is the effective contact rate of individuals that were infected  days ago ( is the duration of infection, in days),  is the number of susceptible individuals,   is the number of infected individuals that were infected  days ago,  is the total size of the jurisdiction's population.An implication of the above equation is that we assume homogeneous mixing among individuals, which means we do not account for age-or location-based heterogeneities in transmission.
After being infected, individuals transition into the infected class  1 , where the change in individuals infected  = 1 day ago is and the change in individuals infected  = 2, … ,  days ago is where  is the duration an individual spends in each infectious compartment (i.e., one day).Note that we implicitly account for an exposed (E) class in the above infectious compartments by setting   = 0 for  = 1, … , ℓ, where ℓ <  is the length (in days) of the latent period.
At the beginning of the analysis, the jurisdiction's fully protected population (i.e., the "removed" individuals) is defined as the sum of (i) the individuals that were fully vaccinated within six months of the start date of the period of analysis and (ii) the individuals that were infected within the last six months (regardless of whether they were vaccinated).There are a few implicit assumptions that are embedded in this calculation.First, we assumed that both naturally acquired and vaccine-acquired immunity last for 180 days [36] and provide the same level of immunity.
Second, the risk of getting infected is the same for individuals that were never vaccinated and for individuals who were vaccinated more than six months ago.Third, the likelihood of getting vaccinated is the same regardless of whether an individual was previously infected or not.Fourth, there is no partial immunity (i.e., individuals are either fully protected or fully susceptible), and previously infected and vaccinated individuals cannot revert to being susceptible during the analytic periods analyzed (i.e., the effects of waning immunity are insignificant over the 56 days of our study period).As a result, the number of removed individuals in the jurisdiction's population changes according to  ̇=   since immunity through vaccination is assumed constant over the period analyzed.

Modeling Nonpharmaceutical Interventions
The above model of disease transmission can be modified to account for transmission reductions due to CICT and other NPIs (see Figure S2 for a schematic representation of the disease transmission model with CICT).While non-CICT NPIs reduce by a certain proportion each of the contact rates   , CICT makes certain   's equal to zero with isolation or quarantine of infected individuals.Using the above notation, the basic reproduction number,  0 , of this model is The model can disentangle the public health impact of the CICT program from the public health impact of all other NPIs.Transmission reductions from the CICT program (i.e., CICT effectiveness) were calculated by inputting in the model two key jurisdiction-specific "performance values" which are: (i) the proportion of cases and contacts that entered isolation and quarantine, and (ii) the days required to do so (see Table S2).By assuming certain levels of compliance with isolation and quarantine guidelines, we can use the SIR model described above to obtain an estimate of the number of COVID-19 cases averted by CICT by comparing the scenarios with and without the CICT program.That is, in the case where there is no CICT program, we assume that the only cases and contacts who entered isolation and quarantine are those who did so voluntarily, and we compare the cumulative number of cases between the two scenarios.
This way of calculating the public health impact of the CICT program implicitly implies that some cases and contacts would only go into isolation or quarantine if they had been interviewed or notified by the CICT program, and that the effect of the CICT program on the reduction in disease transmission is constant over the entire study period (56 days).
In addition to the transmission reduction due to the CICT program, there are also other, non-CICT, NPIs that might affect COVID-19 transmission (e.g., school and business closures, large gathering restrictions).We estimate their impact on transmission reduction by fitting the curve of cumulative cases modeled with the above epidemiological model to the jurisdiction's reported cumulative cases.Essentially, what we are doing here is adding a value between 0 and 1 that multiplies each of the contact rates   .The value that minimized the deviation (i.e., the transmission reduction that minimized the mean squared error) between the fitted and reported cumulative case curves is the estimate of the effectiveness of other NPIs.This fitting process gives us an estimated percentage reduction in transmission attributable to other NPIs.An implication of this fitting process is that the effects of other NPIs are implicitly constant over the entire study period (56 days).

Figure S1 .
Figure S1.Number of cases averted and corresponding percentage reduction in COVID-19 cases by

Table S1 .
COVID-19 test positivity and share of polymerase chain reaction (PCR) tests in Vermont.Data were provided directly from the Vermont Department of Health.
* Mean daily incidence for the 56 days starting from the beginning of the evaluation.†% of contacts identified = # of named contacts / expected # of contacts per case.Expected # of contacts per case = # of reported cases * average # named contacts per case ‡ This is the reported median days from specimen collection to positive test results reported to health departments.§This is the reported median days from specimen collection to contact notification.¶Includingcontactswho later become cases.Calculated as follows using the observed performance metrics in this table, assumed compliance with isolation and quarantine guidance among cases and contacts in TableS4, and an assumed k=1.2:[(%Cases interviewed * Compliance) + k * % Contacts identified * (% Contacts monitored * Compliance + % Contacts notified but not monitored * Compliance)] /(1+k)

Table S3 .
Comparison of CICT's Impact on COVID-19 cases for 2-day and 3-day latent duration, Results corresponding to a 2-day latent period associated with the Delta strain in the base case-see Table2.† The number of cases averted by CICT among every 100 cases not averted by vaccine or other nonpharmaceutical interventions (NPIs; such as facemask policies, large gathering restrictions, and school/business closures). * not notified by the Vermont Department of Health did not isolate or quarantine.We varied these assumed values to see how this impacts the number of cases averted by the CICT program.First, we found that reducing the assumed percentage of cases who adhere to isolation guidelines by 20 a. Cases Averted by CICT as a Function of Baseline Vaccination Coverage

Table S4 .
Assumed proportions of confirmed cases and their contacts that effectively isolated or quarantined * Confirmed

Table S5 .
Comparison of CICT's impact during various periods in Vermont under different assumed public compliance with isolation and quarantine guidelines.

Table S6 .
Comparison of CICT's Impact for various median number of days from infection to Results corresponding to the observed median days from infection to isolation during this period-see Table2.† Equivalent to the number of cases averted by CICT among every 100 cases not averted by vaccine or other nonpharmaceutical interventions (NPIs; such as facemask policies, large gathering restrictions, and school/business closures). *